Structuring your solutions is crucial; breaking down large solution sets makes them more manageable and easier to tackle.

The five effective prompting techniques are: 1) Few-shot prompting, where you give examples of what good looks like; 2) Decomposition, which involves solving sub-problems first; 3) Self-criticism, where you check and reflect on your response; 4) Providing additional context about the problem; and 5) Ensembling, where you try different roles and models to find common answers.

Software design is fundamentally about decomposing complex systems into smaller, manageable units that can be implemented independently.

When faced with multiple potential solutions, breaking them down makes the challenges more manageable, but prioritization is essential due to limited resources.

Treating the model as capable and giving it complex instructions can lead to better outcomes than simplifying the task.

With lean programming, you can create a blueprint for a problem, allowing for divide and conquer approaches where separate parts can be worked on independently.

To improve AI output, it's essential to break down complex tasks into smaller steps, as suggested by Jake Heller's approach.

The outcome is better if you structure the reasoning and help iterate with the model on how it should do reasoning.

In collaboration, there's often a brainstorming process where unrealistic assumptions are made to explore potential solutions before breaking the problem into smaller sub-problems.